

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			return ConvertCommand(
			    args.model_type  ,	args.tf_checkpoint  ,	args.pytorch_dump_output  ,	args.config  ,	args.finetuning_task_name)


__snake_case				:      Dict   		=			"""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""



class        A__(a_							):
						"""simple docstring"""






						@staticmethod
						def 			UpperCamelCase__					(    _lowercase    )     -> Optional[int]:
									a_						:						int 							= parser.add_parser(
									    """convert"""  ,      help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""  ,      )
									train_parser.add_argument("""--model_type"""  ,      type=_lowercase  ,      required=_lowercase  ,      help="""Model's type."""    )
									train_parser.add_argument(
									    """--tf_checkpoint"""  ,      type=_lowercase  ,      required=_lowercase  ,      help="""TensorFlow checkpoint path or folder."""    )
									train_parser.add_argument(
									    """--pytorch_dump_output"""  ,      type=_lowercase  ,      required=_lowercase  ,      help="""Path to the PyTorch saved model output."""    )
									train_parser.add_argument("""--config"""  ,      type=_lowercase  ,      default=""""""  ,      help="""Configuration file path or folder."""    )
									train_parser.add_argument(
									    """--finetuning_task_name"""  ,      type=_lowercase  ,      default=_lowercase  ,      help="""Optional fine-tuning task name if the TF model was a finetuned model."""  ,      )
									train_parser.set_defaults(func=_lowercase    )





						def __init__(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      *_lowercase  ,      )     -> Tuple:
									a_						:						List[str] 							= logging.get_logger("""transformers-cli/converting"""    )

									self._logger.info(F'''Loading model {model_type}'''    )
									a_						:						Any 							= model_type
									a_						:						List[Any] 							= tf_checkpoint
									a_						:						str 							= pytorch_dump_output
									a_						:						Dict 							= config
									a_						:						Union[str, Any] 							= finetuning_task_name





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									if self._model_type == "albert":
												try:
															from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
												except ImportError:
															raise ImportError(_lowercase    )

												convert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._config  ,      self._pytorch_dump_output    )
									elif self._model_type == "bert":
												try:
															from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
												except ImportError:
															raise ImportError(_lowercase    )

												convert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._config  ,      self._pytorch_dump_output    )
									elif self._model_type == "funnel":
												try:
															from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
												except ImportError:
															raise ImportError(_lowercase    )

												convert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._config  ,      self._pytorch_dump_output    )
									elif self._model_type == "t5":
												try:
															from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
												except ImportError:
															raise ImportError(_lowercase    )

												convert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._config  ,      self._pytorch_dump_output    )
									elif self._model_type == "gpt":
												from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
												    convert_openai_checkpoint_to_pytorch,
												)

												convert_openai_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._config  ,      self._pytorch_dump_output    )
									elif self._model_type == "transfo_xl":
												try:
															from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
															    convert_transfo_xl_checkpoint_to_pytorch,
															)
												except ImportError:
															raise ImportError(_lowercase    )

												if "ckpt" in self._tf_checkpoint.lower():
															a_						:						Tuple 							= self._tf_checkpoint
															a_						:						str 							= """"""
												else:
															a_						:						Optional[int] 							= self._tf_checkpoint
															a_						:						int 							= """"""
												convert_transfo_xl_checkpoint_to_pytorch(
												    _lowercase  ,      self._config  ,      self._pytorch_dump_output  ,      _lowercase    )
									elif self._model_type == "gpt2":
												try:
															from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
															    convert_gpta_checkpoint_to_pytorch,
															)
												except ImportError:
															raise ImportError(_lowercase    )

												convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._config  ,      self._pytorch_dump_output    )
									elif self._model_type == "xlnet":
												try:
															from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
															    convert_xlnet_checkpoint_to_pytorch,
															)
												except ImportError:
															raise ImportError(_lowercase    )

												convert_xlnet_checkpoint_to_pytorch(
												    self._tf_checkpoint  ,      self._config  ,      self._pytorch_dump_output  ,      self._finetuning_task_name    )
									elif self._model_type == "xlm":
												from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
												    convert_xlm_checkpoint_to_pytorch,
												)

												convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._pytorch_dump_output    )
									elif self._model_type == "lxmert":
												from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
												    convert_lxmert_checkpoint_to_pytorch,
												)

												convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._pytorch_dump_output    )
									elif self._model_type == "rembert":
												from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
												    convert_rembert_tf_checkpoint_to_pytorch,
												)

												convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,      self._config  ,      self._pytorch_dump_output    )
									else:
												raise ValueError(
												    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""    )





import math


def 					_UpperCAmelCase				(						a__ = 1_0_0):

			'''simple docstring'''



			a_						:						List[str] 							= sum(i * i for i in range(1  ,	n + 1))
			a_						:						Optional[Any] 							= int(math.pow(sum(range(1  ,	n + 1))  ,	2))
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")


